transient-showcase
nbdev
transient-showcase | nbdev | |
---|---|---|
9 | 45 | |
177 | 4,772 | |
7.9% | 1.2% | |
7.9 | 8.5 | |
4 months ago | 7 days ago | |
Emacs Lisp | Jupyter Notebook | |
GNU General Public License v3.0 only | Apache License 2.0 |
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transient-showcase
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More advanced emacs tutorials
Here's a guide to programming with transient I wrote a while back. There's a lot going on. The guide itself is literate org and meant to be interactively consumed: https://github.com/positron-solutions/transient-showcase
- transient-showcase: Example forms for transient UI's in Emacs
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What is literate programming used for?
A literate org guide to transient programming that consists of an org file you can read through, play with each individual sample, or load as a module from the tangled result to just see the behaviors in action.
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Transient Demo Requests?
Since 0.4.0 shipped, I updated the accompanying guide I wrote.
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Transient v0.4.0 released
Sounds like time for me to update the transient showcase.
- How to unwind-protect a transient?
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Transient :incompatible help
I've tried writing a transient prefix using the :incompatible key, following the example in https://github.com/positron-solutions/transient-showcase
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[ANN] Transient Showcase
A literate org guide with over 20 simple examples illustrating different behaviors.
nbdev
- The Jupyter+Git problem is now solved
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What is literate programming used for?
One example I've seen is ML/DL folks using jupyter notebooks to develop DL libraries in jupyter notebooks, see https://github.com/fastai/nbdev
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GitHub Accelerator: our first cohort and what's next
- https://github.com/fastai/nbdev: Increase developer productivity by 10x with a new exploratory programming workflow.
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Startups are in first batch of GitHub OS Accelerator
9. Nbdev: Boost developer productivity with an exploratory programming workflow - https://nbdev.fast.ai/
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Start learning python for a Statistician with SAS experience and little R experience
See if you like nbdev way of working with data through python and jupyter. nbdev is an optional part that will create python packages from jupyter notebooks. Also even the simple tutorials are opinionated and will guide you to unit test your code and write CICD pipelines.
- FastKafka - free open source python lib for building Kafka-based services
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isn't this just too much for a take home assignment?
You probably don’t have time for this for the purposes of your task, but I will also throw in the recommendation of nbdev especially if you’re a Python person. I haven’t had a project to use it on yet, but I’ve gone through the docs and the walkthrough and it seems like a great framework for starting potential projects with all the infrastructure needed for if/when they eventually get big and need all the packaging and stuff
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Any experience dealing with a non-technical manager?
nbdev: jupyter notebooks -> python package
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Resources to bridge the gap between jupyter notebooks and regular python development
Take a look at https://github.com/fastai/nbdev - haven't used it but supposedly the whole if fast.ai library was written that way. It sounds like a natural direction in your scenario - allowing your to keep working in a familiar environment and still producing production ready code (will, at least in paper 😅)
- Rant: Jupyter notebooks are trash.
What are some alternatives?
transient - Transient commands
papermill - 📚 Parameterize, execute, and analyze notebooks
makey - Flexible context menu system
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
discover.el - Discover more of emacs with context menus!
dbt - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications. [Moved to: https://github.com/dbt-labs/dbt-core]
smug - Super Monadic Über Go-into : parser combinators for Common Lisp
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
rr - Record and Replay Framework
Jupyter-PowerShell - Jupyter Kernel for PowerShell
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
black - The uncompromising Python code formatter